Removal of movement-induced EEG artifacts: current state of the art and guidelines

Metastability in the wild: A scoping review of empirical neuroimaging studies in humans

Metastability is proposed as the mechanism supporting our adaptive responses to the environment. While extensive research has characterized brain metastability during rest and task performance, prior studies have mainly focused on understanding underlying mechanisms, with limited exploration of its application in mental processes and behaviors. This scoping review offers an overview of the existing empirical literature in this area. Through a systematic search that included 36 articles, our results reveal a predominance of resting-state fMRI studies, variability in how metastability is defined, and a lack of consideration for common confounds in neuroimaging data. The review concludes with suggestions for future research directions to address crucial unresolved issues in the field.

Response coupling with an auxiliary neural signal for enhancing brain signal detection

Brain-computer interfaces (BCIs) offer an implicit, non-linguistic communication channel between users and machines. Despite their potential, BCIs are far from becoming a mainstream communication modality like text and speech. While non-invasive BCIs, such as Electroencephalography, are favored for their ease of use, their broader adoption is limited by challenges related to signal noise, artifacts, and variability across users. In this paper, we propose a novel method called response coupling, aimed at enhancing brain signal detection and reliability by pairing a brain signal with an artificially induced auxiliary signal and leveraging their interaction. Specifically, we use error-related potentials (ErrPs) as the primary signal and steady-state visual evoked potentials (SSVEPs) as the auxiliary signal. SSVEPs, known for their phase-locked responses to rhythmic stimuli, are selected because rhythmic neural activity plays a critical role in sensory and cognitive processes, with evidence suggesting that reinforcing these oscillations can improve neural performance. By exploring the interaction between these two signals, we demonstrate that response coupling significantly improves the detection accuracy of ErrPs, especially in the parietal and occipital regions. This method introduces a new paradigm for enhancing BCI performance, where the interaction between a primary and an auxiliary signal is harnessed to enhance the detection performance. Additionally, the phase-locking properties of SSVEPs allow for unsupervised rejection of suboptimal data, further increasing BCI reliability.

FLANet: A multiscale temporal convolution and spatial-spectral attention network for EEG artifact removal with adversarial training

Objective.Denoising artifacts, such as noise from muscle or cardiac activity, is a crucial and ubiquitous concern in neurophysiological signal processing, particularly for enhancing the signal-to-noise ratio in electroencephalograph (EEG) analysis. Novel methods based on deep learning demonstrate a notably prominent effect compared to traditional denoising approaches. However, those still suffer from certain limitations. Some methods often neglect the multi-domain characteristics of the artifact signal. Even among those that do consider these, there are deficiencies in terms of efficiency, effectiveness and computation cost.Approach.In this study, we propose a multiscale temporal convolution and spatial-spectral attention network with adversarial training for automatically filtering artifacts, named filter artifacts network (FLANet). The multiscale convolution module can extract sufficient temporal information and the spatial-spectral attention network can extract not only non-local similarity but also spectral dependencies. To make data denoising more efficient and accurate, we adopt adversarial training with novel loss functions to generate outputs that are closer to pure signals.Main results.The results show that the method proposed in this paper achieves great performance in artifact removal and valid information preservation on EEG signals contaminated by different types of artifacts. This approach enables a more optimal trade-off between denoising efficacy and computational overhead.Significance.The proposed artifact removal framework facilitates the implementation of an efficient denoising method, contributing to the advancement of neural analysis and neural engineering, and can be expected to be applied to clinical research and to realize novel human-computer interaction systems.

Cortical changes associated with an anterior cruciate ligament injury may retrograde skilled kicking in football: preliminary EEG findings

Anterior cruciate ligament injuries (ACLi) impact football players substantially leading to performance declines and premature career endings. Emerging evidence suggests that ACLi should be viewed not merely as peripheral injuries but as complex conditions with neurophysiological aspects. The objective of the present study was to compare kicking performance and associated cortical activity between injured and healthy players. Ten reconstructed and 15 healthy players performed a kicking task. Kicking biomechanics were recorded using wearable inertial measurement unit sensors. Cortical activity was captured with a 64-electrode mobile electroencephalography. Multiscale entropy (MSE) analysis of biomechanics revealed increased variability in foot external rotation among injured players. Source-derived event-related spectral perturbations indicated significant differences in posterior alpha and frontal theta oscillations between the two groups. Furthermore, kick-related complexity of these regions as indexed by MSE was reduced in injured players at medium and coarse scales. Our findings suggest sensorimotor changes during kicking in injured players, which may necessitate compensatory strategies involving augmented attention at the cost of processing visuospatial information. This conflict may hinder the integration of task-relevant information across distributed networks. Our study provides preliminary insights into the neurophysiological implications of ACLi within football context and underscores the potential for prospective research.

PaFESD: Patterns Augmented by Features Epileptic Seizure Detection

Thedetection of epileptic seizures can have a significant impact on the patients' quality of life and on their caregivers. In this paper we propose a method for detecting such seizures from electroencephalogram (EEG) data named Patterns augmented by Features Epileptic Seizure Detection (PaFESD). The main novelty of our proposal consists in a detection model that combines EEG signal features with pattern matching. After cleaning the signal and removing artifacts (as eye blinking or muscle movement noise), time-domain and frequency-domain features are extracted to filter out non-seizure regions of the EEG. Jointly, pattern matching based on Dynamic Time Warping (DTW) distance is also leveraged to identify the most discriminative patterns of the seizures, even under scarce training data. The proposed model is evaluated on all patients in the CHB-MIT database, and the results show that it is able to detect seizures with an average score of 98.9%. Furthermore, our approach achieves a score of 100% (no false alarms or missed true seizures) for 20 of the patients (out of 24). Additionally, we automatically detect the most seizure/non-seizure discriminative EEG channel so that a wearable with only two electrodes would suffice to warn patients of seizures.

Electrocortical activity during resistance exercises in healthy young adults-a systematic review

Introduction

Resistance training (RT) is known to induce both peripheral and central adaptations, resulting in enhanced strength, sports performance, and health benefits. These adaptations are specific to the training stimuli. The acute cortical mechanisms of single sessions resistance exercise (RE) are not yet understood. Therefore, this review investigates the electrocortical activity during acute RE regarding the specific RE stimuli.

Methods

A systematic literature search was conducted across three databases, focusing on the acute electrocortical activity associated with the muscle contraction type, load, and volume of RE in healthy young adults.

Results

Out of an initial 1,332 hits, 19 studies were included for data synthesis. The findings from these studies show that the RE load, contraction type, and volume during RE significantly affect brain activity. The current literature exhibits methodological heterogeneity attributed to variations in study quality, differences in the location of cortical sources, the cortical outcome parameter and the use of diverse training interventions.

Discussion

Despite inconsistencies in the current literature, this review highlights the need to investigate time and frequency-specific characteristics when examining electrocortical activity during RE. More research is necessary to further explore the acute cortical mechanisms related to resistance exercise. Future research could improve our understanding of acute neural responses to RE and provide insights into mechanism underlying more long-term neuroplastic adaptations to RT.

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration

OBJECTIVE

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications.

METHODS

We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data.

RESULTS

DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm.

CONCLUSION

The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration.

SIGNIFICANCE

This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

Searching Across Realities: Investigating ERPs and Eye-Tracking Correlates of Visual Search in Mixed Reality

Mixed Reality allows us to integrate virtual and physical content into users' environments seamlessly. Yet, how this fusion affects perceptual and cognitive resources and our ability to find virtual or physical objects remains uncertain. Displaying virtual and physical information simultaneously might lead to divided attention and increased visual complexity, impacting users' visual processing, performance, and workload. In a visual search task, we asked participants to locate virtual and physical objects in Augmented Reality and Augmented Virtuality to understand the effects on performance. We evaluated search efficiency and attention allocation for virtual and physical objects using event-related potentials, fixation and saccade metrics, and behavioral measures. We found that users were more efficient in identifying objects in Augmented Virtuality, while virtual objects gained saliency in Augmented Virtuality. This suggests that visual fidelity might increase the perceptual load of the scene. Reduced amplitude in distractor positivity ERP, and fixation patterns supported improved distractor suppression and search efficiency in Augmented Virtuality. We discuss design implications for mixed reality adaptive systems based on physiological inputs for interaction.

Reliable electrocortical dynamics of target-directed pass-kicks

Football is one of the most played sports in the world and kicking with adequate accuracy increases the likelihood of winning a competition. Although studies with different target-directed movements underline the role of distinctive cortical activity on superior accuracy, little is known about cortical dynamics associated with kicking. Mobile electroencephalography is a popular tool to investigate cortical modulations during movement, however, inherent and artefact-related pitfalls may obscure the reliability of functional sources and their activity. The purpose of this study was therefore to describe consistent cortical dynamics underlying target-directed pass-kicks based on test-retest reliability estimates. Eleven participants performed a target-directed kicking task at two different sessions within one week. Electroencephalography was recorded using a 65-channel mobile system and behavioural data were collected including motion range, acceleration and accuracy performance. Functional sources were identified using independent component analysis and clustered in two steps with the components of first and subsequently both sessions. Reliability estimates of event-related spectral perturbations were computed pixel-wise for participants contributing with components of both sessions. The parieto-occipital and frontal clusters were reproducible for the same majority of the sample at both sessions. Their activity showed consistent alpha desyhronization and theta sychnronisation patterns with substantial reliability estimates revealing visual and attentional demands in different phases of kicking. The findings of our study reveal prominent cortical demands during the execution of a target-directed kick which may be considered in practical implementations and provide promising academic prospects in the comprehension and investigation of cortical activity associated with target-directed movements.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10094-0.

Electrophysiological (EEG) microstates during dream-like bizarre experiences in a naturalistic scenario using immersive virtual reality

Monitoring the reality status of conscious experience is essential for a human being to interact successfully with the external world. Despite its importance for everyday functioning, reality monitoring can systematically become erroneous, for example, while dreaming or during hallucinatory experiences. To investigate brain processes associated with reality monitoring occurring online during an experience, i.e., perceptual reality monitoring, we assessed EEG microstates in healthy, young participants. In a within-subjects design, we compared the experience of reality when being confronted with dream-like bizarre elements versus realistic elements in an otherwise highly naturalistic real-world scenario in immersive virtual reality. Dream-like bizarreness induced changes in the subjective experience of reality and bizarreness, and led to an increase in the contribution of a specific microstate labelled C'. Microstate C' was related to the suspension of disbelief, i.e. the suppression of bizarre mismatches. Together with the functional interpretation of microstate C' as reported by previous studies, the findings of this study point to the importance of prefrontal meta-conscious control processes in perceptual reality monitoring.

Motion Artifacts in Dynamic EEG Recordings: Experimental Observations, Electrical Modelling, and Design Considerations

Despite the progress in the development of innovative EEG acquisition systems, their use in dynamic applications is still limited by motion artifacts compromising the interpretation of the collected signals. Therefore, extensive research on the genesis of motion artifacts in EEG recordings is still needed to optimize existing technologies, shedding light on possible solutions to overcome the current limitations. We identified three potential sources of motion artifacts occurring at three different levels of a traditional biopotential acquisition chain: the skin-electrode interface, the connecting cables between the detection and the acquisition systems, and the electrode-amplifier system. The identified sources of motion artifacts were modelled starting from experimental observations carried out on EEG signals. Consequently, we designed customized EEG electrode systems aiming at experimentally disentangling the possible causes of motion artifacts. Both analytical and experimental observations indicated two main residual sites responsible for motion artifacts: the connecting cables between the electrodes and the amplifier and the sudden changes in electrode-skin impedance due to electrode movements. We concluded that further advancements in EEG technology should focus on the transduction stage of the biopotentials amplification chain, such as the electrode technology and its interfacing with the acquisition system.

Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol

Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.

Imagined speech classification exploiting EEG power spectrum features

Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces

Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

A Dynamic Evaluation-Denoising Network for Motion Artifacts Removal from Single-Channel EEG

Brain-computer interfaces (BCIs) have gained significant attention in rehabilitation research as a critical step in investigating neural remodeling techniques. However, most existing methods usually overlook the randomness and diversity of motion artifacts, thereby lacking the desired generalization ability and denoising precision, which limits their practical application. To address these limitations, we propose a Dynamic Evaluation Denoising Network (DED-Net) that incorporates an evaluation model with cross-domain feature fusion for artifact detection and classification. Then dynamically selecting Bidirectional Long Short-Term Memory (Bi-LSTM) networks with varying parameters for artifact removal, which achieves superior performance compared to state-of-the-art methods. Our experiment on a semi-simulated dataset constructed by EEGdenoiseNET demonstrates that the performance of DED-Net is advanced over the state-of-the-art method, i.e., SDNet, in terms of the signal-to-noise rate (SNR) and correlation coefficient (CC). Using our method, SNR and CC are 6.0597 dB and 95.28%, respectively increasing by 20.48% and 3.15%. Experiments on real EEG data demonstrate the superior performance of the proposed method in reconstructing EEG signals, in terms of the intent recognition tasks, achieving a remarkable accuracy of 88.89%, outperforming other methods.

Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA

Introduction

To understand brain function in natural real-world settings, it is crucial to acquire brain activity data in noisy environments with diverse artifacts. Electroencephalography (EEG), while susceptible to environmental and physiological artifacts, can be cleaned using advanced signal processing techniques like Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). This study aims to demonstrate that ASR and ICA can effectively extract brain activity from the substantial artifacts occurring while skateboarding on a half-pipe ramp.

Methods

A dual-task paradigm was used, where subjects were presented with auditory stimuli during skateboarding and rest conditions. The effectiveness of ASR and ICA in cleaning artifacts was evaluated using a support vector machine to classify the presence or absence of a sound stimulus in single-trial EEG data. The study evaluated the effectiveness of ASR and ICA in artifact cleaning using five different pipelines: (1) Minimal cleaning (bandpass filtering), (2) ASR only, (3) ICA only, (4) ICA followed by ASR (ICAASR), and (5) ASR preceding ICA (ASRICA). Three skateboarders participated in the experiment.

Results

Results showed that all ICA-containing pipelines, especially ASRICA (69%, 68%, 63%), outperformed minimal cleaning (55%, 52%, 50%) in single-trial classification during skateboarding. The ASRICA pipeline performed significantly better than other pipelines containing ICA for two of the three subjects, with no other pipeline performing better than ASRICA. The superior performance of ASRICA likely results from ASR removing non-stationary artifacts, enhancing ICA decomposition. Evidenced by ASRICA identifying more brain components via ICLabel than ICA alone or ICAASR for all subjects. For the rest condition, with fewer artifacts, the ASRICA pipeline (71%, 82%, 75%) showed slight improvement over minimal cleaning (73%, 70%, 72%), performing significantly better for two subjects.

Discussion

This study demonstrates that ASRICA can effectively clean artifacts to extract single-trial brain activity during skateboarding. These findings affirm the feasibility of recording brain activity during physically demanding tasks involving substantial body movement, laying the groundwork for future research into the neural processes governing complex and coordinated body movements.

Advancing passive BCIs: a feasibility study of two temporal derivative features and effect size-based feature selection in continuous online EEG-based machine error detection

The emerging integration of Brain-Computer Interfaces (BCIs) in human-robot collaboration holds promise for dynamic adaptive interaction. The use of electroencephalogram (EEG)-measured error-related potentials (ErrPs) for online error detection in assistive devices offers a practical method for improving the reliability of such devices. However, continuous online error detection faces challenges such as developing efficient and lightweight classification techniques for quick predictions, reducing false alarms from artifacts, and dealing with the non-stationarity of EEG signals. Further research is essential to address the complexities of continuous classification in online sessions. With this study, we demonstrated a comprehensive approach for continuous online EEG-based machine error detection, which emerged as the winner of a competition at the 32nd International Joint Conference on Artificial Intelligence. The competition consisted of two stages: an offline stage for model development using pre-recorded, labeled EEG data, and an online stage 3 months after the offline stage, where these models were tested live on continuously streamed EEG data to detect errors in orthosis movements in real time. Our approach incorporates two temporal-derivative features with an effect size-based feature selection technique for model training, together with a lightweight noise filtering method for online sessions without recalibration of the model. The model trained in the offline stage not only resulted in a high average cross-validation accuracy of 89.9% across all participants, but also demonstrated remarkable performance during the online session 3 months after the initial data collection without further calibration, maintaining a low overall false alarm rate of 1.7% and swift response capabilities. Our research makes two significant contributions to the field. Firstly, it demonstrates the feasibility of integrating two temporal derivative features with an effect size-based feature selection strategy, particularly in online EEG-based BCIs. Secondly, our work introduces an innovative approach designed for continuous online error prediction, which includes a straightforward noise rejection technique to reduce false alarms. This study serves as a feasibility investigation into a methodology for seamless error detection that promises to transform practical applications in the domain of neuroadaptive technology and human-robot interaction.

An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study

Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task.

Using mobile eye tracking to measure cognitive load through gaze behavior during walking in lower limb prosthesis users: A preliminary assessment

BACKGROUND

Lower limb amputation does not affect only physical and psychological functioning but the use of a prosthetic device can also lead to increased cognitive demands. Measuring cognitive load objectively is challenging, and therefore, most studies use questionnaires that are easy to apply but can suffer from subjective bias. Motivated by this, the present study investigated whether a mobile eye tracker can be used to objectively measure cognitive load by monitoring gaze behavior during a set of motor tasks.

METHODS

Five prosthetic users and eight able-bodied controls participated in this study. Eye tracking data and kinematics were recorded during a set of motor tasks (level ground walking, walking on uneven terrain, obstacle avoidance, stairs up and ramp down, as well as ramp up and stairs down) while the participants were asked to focus their gaze on a visual target for as long as possible. Target fixation times and increase in pupil diameters were determined and correlated to subjective ratings of cognitive load.

FINDINGS

Overall, target fixation time and pupil diameter showed strong negative and positive correlations, respectively, to the subjective rating of cognitive load in the able-bodied controls (-0.75 and 0.80, respectively). However, the individual correlation strength, and in some cases, even the sign, was different across participants. A similar trend could be observed in prosthetic users.

INTERPRETATION

The results of this study showed that a mobile eye tracker may be used to estimate cognitive load in prosthesis users during locomotor tasks. This paves the way to establish a new approach to assessing cognitive load, which is objective and yet practical and simple to administer. Nevertheless, future studies should corroborate these results by comparing them to other objective measures as well as focus on translating the proposed approach outside of a laboratory.

Auditory Cue Effects on Gait-Phase-Dependent Electroencephalogram (EEG) Modulations during Overground and Treadmill Walking

Walking rehabilitation following injury or disease involves voluntary gait modification, yet the specific brain signals underlying this process remains unclear. This aim of this study was to investigate the impact of an auditory cue on changes in brain activity when walking overground (O) and on a treadmill (T) using an electroencephalogram (EEG) with a 32-electrode montage. Employing a between-group repeated-measures design, 24 participants (age: 25.7 ± 3.8 years) were randomly allocated to either an O (n = 12) or T (n = 12) group to complete two walking conditions (self-selected speed control (sSC) and speed control (SC)). The differences in brain activities during the gait cycle were investigated using statistical non-parametric mapping (SnPM). The addition of an auditory cue did not modify cortical activity in any brain area during the gait cycle when walking overground (all p > 0.05). However, significant differences in EEG activity were observed in the delta frequency band (0.5-4 Hz) within the sSC condition between the O and T groups. These differences occurred at the central frontal (loading phase) and frontocentral (mid stance phase) brain areas (p < 0.05). Our data suggest auditory cueing has little impact on modifying cortical activity during overground walking. This may have practical implications in neuroprosthesis development for walking rehabilitation, sports performance optimization, and overall human quality-of-life improvement.

Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features-A mini-review

In today's digital information age, human exposure to visual artifacts has reached an unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the status of artworks which indicates a special appreciation of these objects. For many persons, the perception of such artworks coincides with aesthetic experiences (AE) that can positively affect health and wellbeing. AEs are composed of complex cognitive and affective mental and physiological states. More profound scientific understanding of the neural dynamics behind AEs would allow the development of passive Brain-Computer-Interfaces (BCI) that offer personalized art presentation to improve AE without the necessity of explicit user feedback. However, previous empirical research in visual neuroaesthetics predominantly investigated functional Magnetic Resonance Imaging and Event-Related-Potentials correlates of AE in unnaturalistic laboratory conditions which might not be the best features for practical neuroaesthetic BCIs. Furthermore, AE has, until recently, largely been framed as the experience of beauty or pleasantness. Yet, these concepts do not encompass all types of AE. Thus, the scope of these concepts is too narrow to allow personalized and optimal art experience across individuals and cultures. This narrative mini-review summarizes the state-of-the-art in oscillatory Electroencephalography (EEG) based visual neuroaesthetics and paints a road map toward the development of ecologically valid neuroaesthetic passive BCI systems that could optimize AEs, as well as their beneficial consequences. We detail reported oscillatory EEG correlates of AEs, as well as machine learning approaches to classify AE. We also highlight current limitations in neuroaesthetics and suggest future directions to improve EEG decoding of AE.

A review of combined functional neuroimaging and motion capture for motor rehabilitation

BACKGROUND

Technological advancements in functional neuroimaging and motion capture have led to the development of novel methods that facilitate the diagnosis and rehabilitation of motor deficits. These advancements allow for the synchronous acquisition and analysis of complex signal streams of neurophysiological data (e.g., EEG, fNIRS) and behavioral data (e.g., motion capture). The fusion of those data streams has the potential to provide new insights into cortical mechanisms during movement, guide the development of rehabilitation practices, and become a tool for assessment and therapy in neurorehabilitation.

RESEARCH OBJECTIVE

This paper aims to review the existing literature on the combined use of motion capture and functional neuroimaging in motor rehabilitation. The objective is to understand the diversity and maturity of technological solutions employed and explore the clinical advantages of this multimodal approach.

METHODS

This paper reviews literature related to the combined use of functional neuroimaging and motion capture for motor rehabilitation following the PRISMA guidelines. Besides study and participant characteristics, technological aspects of the used systems, signal processing methods, and the nature of multimodal feature synchronization and fusion were extracted.

RESULTS

Out of 908 publications, 19 were included in the final review. Basic or translation studies were mainly represented and based predominantly on healthy participants or stroke patients. EEG and mechanical motion capture technologies were most used for biomechanical data acquisition, and their subsequent processing is based mainly on traditional methods. The system synchronization techniques at large were underreported. The fusion of multimodal features mainly supported the identification of movement-related cortical activity, and statistical methods were occasionally employed to examine cortico-kinematic relationships.

CONCLUSION

The fusion of motion capture and functional neuroimaging might offer advantages for motor rehabilitation in the future. Besides facilitating the assessment of cognitive processes in real-world settings, it could also improve rehabilitative devices' usability in clinical environments. Further, by better understanding cortico-peripheral coupling, new neuro-rehabilitation methods can be developed, such as personalized proprioceptive training. However, further research is needed to advance our knowledge of cortical-peripheral coupling, evaluate the validity and reliability of multimodal parameters, and enhance user-friendly technologies for clinical adaptation.

EEG Dynamics of Locomotion and Balancing: Solution to Neuro-Rehabilitation

The past decade has witnessed tremendous growth in analyzing the cortical representation of human locomotion and balance using Electroencephalography (EEG). With the advanced developments in miniaturized electronics, wireless brain recording systems have been developed for mobile recordings, such as in locomotion. In this review, the cortical dynamics during locomotion are presented with extensive focus on motor imagery, and employing the treadmill as a tool for performing different locomotion tasks. Further, the studies that examine the cortical dynamics during balancing, focusing on two types of balancing tasks, ie, static and dynamic, with the challenges in sensory inputs and cognition (dual-task), are presented. Moreover, the current literature demonstrates the advancements in signal processing methods to detect and remove the artifacts from EEG signals. Prior studies show the electrocortical sources in the anterior cingulate, posterior parietal, and sensorimotor cortex was found to be activated during locomotion. The event-related potential has been observed to increase in the fronto-central region for a wide range of balance tasks. The advanced knowledge of cortical dynamics during mobility can benefit various application areas such as neuroprosthetics and gait/balance rehabilitation. This review will be beneficial for the development of neuroprostheses, and rehabilitation devices for patients suffering from movement or neurological disorders.

Investigating the EEG Profile of Elite and Non-Elite Players in the Basketball Free Throw Task

This study aimed to investigate the electroencephalographic profile of elite and non-elite basketball players seconds before and during the basketball free throw. Sixteen male subjects in the elite group (national team/premier league players with an average age of 22.06 ± 1.56) and 16 male non-elite subjects (university players with an average age of 22.37 ± 1.45) voluntarily participated in this research. Electroencephalographic data were measured from 28 cortical areas using a mobile wireless device. ANOVA with repeated measures were also performed to investigate the characteristics of theta, alpha, and beta frequency bands. The findings showed the higher cortical activity of the elite group. Different frequency bands exhibited similar asymmetry patterns, suggesting the higher activity of the left hemisphere in most of the homologous sites. Moreover, the activity of frequency bands in the left hemisphere rose by approaching the moment of throw. Furthermore, the activity of a limited number of right hemisphere sites increased by getting closer to the moment of action. In general, hemispheric asymmetry in favor of the left hemisphere has a cortical pattern, reflecting high-performance activities. In addition, the characteristics of different frequency bands of hemispheres are directed toward increasing cognitive processing, attention focusing, and inhibiting irrelevant information.

Assessing cognitive workloads of assembly workers during multi-task switching

Complex assembly tasks with multiple manual operations and steps often require rapid judgment and action under time pressure and cause most human-related errors. The task switching and action transitions are major sources of these errors. This study intends to implement an electroencephalography (EEG) approach to quantitatively evaluate the mental workload during task switching and transition. The time-frequency and spectrum analysis were utilized to compute and reflect the task demand between the intervals of individual tasks. This study developed an experiment to validate the proposed assessment approach and benchmark the results with the National Aeronautics and Space Administration task load index (NASA-TLX) subjective evaluation scale analysis. The results show that the average value of the power spectral densities (PSDs) of the gamma band signal of the AF4 channel and the beta band signal of Channel F3 show distinctive signal patterns among task stages and intervals. During the interval between the idling stage and the part selection stage, the peak of the PSD envelope increased from 18 to 27 Hz, suggesting advanced cognition increases the mental workload of the interval between different tasks. Therefore, the task switching period cannot be regarded as rest and need to be optimized with better task organization.

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is time/labor-consuming, subjective, and incompatible to filter massive EEG data in real-time. In this paper, we proposed a deep learning framework named Artifact Removal Wasserstein Generative Adversarial Network (AR-WGAN), where the well-trained model can decompose input EEG, detect and delete artifacts, and then reconstruct denoised signals within a short time. The proposed approach was systematically compared with commonly used denoising methods including Denoised AutoEncoder, Wiener Filter, and Empirical Mode Decomposition, with both public and self-collected datasets. The experimental results proved the promising performance of AR-WGAN on automatic artifact removal for massive data across subjects, with correlation coefficient up to 0.726±0.033, and temporal and spatial relative root-mean-square error as low as 0.176±0.046 and 0.761±0.046, respectively. This work may demonstrate the proposed AR-WGAN as a high-performance end-to-end method for EEG denoising, with many on-line applications in clinical EEG monitoring and brain-computer interfaces.

A Dynamical Systems Approach to Characterizing Brain-Body Interactions during Movement: Challenges, Interpretations, and Recommendations

Brain-body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of 'brain' activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a fundamental challenge to the use of BBIs for answering basic and applied research questions in neuroimaging and neurorehabilitation. Thus, this review is written as a tutorial to address both limitations for those interested in studying BBIs through a dynamical systems lens. First, we outline current best practices for acquiring, interpreting, and cleaning scalp-measured electroencephalography (EEG) acquired during whole-body movement. Second, we discuss historical and current theories for modeling EEG and kinematic data as dynamical systems. Third, we provide worked examples from both canonical model systems and from empirical EEG and kinematic data collected from two subjects during an overground walking task.

The neuroscience of music – towards ecological validity

Studies in the neuroscience of music gained momentum in the 1990s as an integrated part of the well-controlled experimental research tradition. However, during the past two decades, these studies have moved toward more naturalistic, ecologically valid paradigms. Here, I introduce this move in three frameworks: (i) sound stimulation and empirical paradigms, (ii) study participants, and (iii) methods and contexts of data acquisition. I wish to provide a narrative historical overview of the development of the field and, in parallel, to stimulate innovative thinking to further advance the ecological validity of the studies without overlooking experimental rigor.

Therapeutic benefits of lower limb prostheses: a systematic review

BACKGROUND

Enhancing the quality of life of people with a lower limb amputation is critical in prosthetic development and rehabilitation. Yet, no overview is available concerning the impact of passive, quasi-passive and active ankle-foot prostheses on quality of life.

OBJECTIVE

To systematically review the therapeutic benefits of performing daily activities with passive, quasi-passive and active ankle-foot prostheses in people with a lower limb amputation.

METHODS

We searched the Pubmed, Web of Science, Scopus and Pedro databases, and backward citations until November 3, 2021. Only English-written randomised controlled trials, cross-sectional, cross-over and cohort studies were included when the population comprised individuals with a unilateral transfemoral or transtibial amputation, wearing passive, quasi-passive or active ankle-foot prostheses. The intervention and outcome measures had to include any aspect of quality of life assessed while performing daily activities. We synthesised the participants' characteristics, type of prosthesis, intervention, outcome and main results, and conducted risk of bias assessment using the Cochrane risk of bias tool. This study is registered on PROSPERO, number CRD42021290189.

RESULTS

We identified 4281 records and included 34 studies in total. Results indicate that quasi-passive and active prostheses are favoured over passive prostheses based on biomechanical, physiological, performance and subjective measures in the short-term. All studies had a moderate or high risk of bias.

CONCLUSION

Compared to passive ankle-foot prostheses, quasi-passive and active prostheses significantly enhance the quality of life. Future research should investigate the long-term therapeutic benefits of prosthetics devices.

Mobile electroencephalography captures differences of walking over even and uneven terrain but not of single and dual-task gait

Walking on natural terrain while performing a dual-task, such as typing on a smartphone is a common behavior. Since dual-tasking and terrain change gait characteristics, it is of interest to understand how altered gait is reflected by changes in gait-associated neural signatures. A study was performed with 64-channel electroencephalography (EEG) of healthy volunteers, which was recorded while they walked over uneven and even terrain outdoors with and without performing a concurrent task (self-paced button pressing with both thumbs). Data from n = 19 participants (M = 24 years, 13 females) were analyzed regarding gait-phase related power modulations (GPM) and gait performance (stride time and stride time-variability). GPMs changed significantly with terrain, but not with the task. Descriptively, a greater beta power decrease following right-heel strikes was observed on uneven compared to even terrain. No evidence of an interaction was observed. Beta band power reduction following the initial contact of the right foot was more pronounced on uneven than on even terrain. Stride times were longer on uneven compared to even terrain and during dual- compared to single-task gait, but no significant interaction was observed. Stride time variability increased on uneven terrain compared to even terrain but not during single- compared to dual-tasking. The results reflect that as the terrain difficulty increases, the strides become slower and more irregular, whereas a secondary task slows stride duration only. Mobile EEG captures GPM differences linked to terrain changes, suggesting that the altered gait control demands and associated cortical processes can be identified. This and further studies may help to lay the foundation for protocols assessing the cognitive demand of natural gait on the motor system.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively. Independent component analysis (ICA) was conducted on the selected epochs of six selected components relevant to the frontal, central, parietal, occipital, left motor, and right motor areas. Granger-Geweke causality (GGC), directed transfer function (DTF), partial directed coherence (PDC), and generalized partial directed coherence (GPDC) brain connectivity estimators were used to calculate the connectivity matrixes. These connectivity matrixes were used as features to train the support vector machine (SVM) with the radial basis function (RBF) and classify the distracted and non-distracted driving tasks. GGC, DTF, PDC, and GPDC connectivity estimators yielded the classification accuracies of 82.27%, 70.02%, 86.19%, and 80.95%, respectively. Further analysis of the PDC connectivity estimator was conducted to determine the best window to differentiate between the distracted and non-distracted driving tasks. This study suggests that the PDC connectivity estimator can yield better classification accuracy for driver distractions.

Exploring how healthcare teams balance the neurodynamics of autonomous and collaborative behaviors: a proof of concept

Team members co-regulate their activities and move together at the collective level of behavior while coordinating their actions toward shared goals. In parallel with team processes, team members need to resolve uncertainties arising from the changing task and environment. In this exploratory study we have measured the differential neurodynamics of seven two-person healthcare teams across time and brain regions during autonomous (taskwork) and collaborative (teamwork) segments of simulation training. The questions posed were: (1) whether these abstract and mostly integrated constructs could be separated neurodynamically; and, (2) what could be learned about taskwork and teamwork by trying to do so? The taskwork and teamwork frameworks used were Neurodynamic Information (NI), an electroencephalography (EEG) derived measure shown to be a neurodynamic proxy for the pauses and hesitations associated with individual uncertainty, and inter-brain EEG coherence (IBC) which is a required component of social interactions. No interdependency was observed between NI and IBC, and second-by-second dynamic comparisons suggested mutual exclusivity. These studies show that proxies for fundamental properties of teamwork and taskwork can be separated neurodynamically during team performances of ecologically valid tasks. The persistent expression of NI and IBC were not simultaneous suggesting that it may be difficult for team members to maintain inter-brain coherence while simultaneously reducing their individual uncertainties. Lastly, these separate dynamics occur over time frames of 15-30 s providing time for real-time detection and mitigation of individual and collaborative complications during training or live patient encounters.

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.